To study effective speech features which can represent different emotion styles in mandarin speech, nonlinear features based on Teager Energy Operator(TEO) are researched. Neutral state and 3 emotional states (i.e. happiness, anger and sadness) are classified from the mandarin speech database. MFCC extraction and HMM-based emotion recognition are used as baseline system to evaluate the emotional classification performance of TEO-based features. In comparison with MFCC, while text-dependent, improvements of classification capacity are obtained when using all 4 nonlinear features (i.e. NFD_Mel , AF_Mel, DAF_Mel, AM_SBCC). While text-independent, the performance of emotion classification are improved by using NFD_Mel , AF_Mel and DAF_Mel , but deteriorated by using AM_SBCC. The results of classification demonstrate that the nonlinear features based on TEO, when using NFD_Mel, AF_Mel and DAF_Mel, are better able to represent different emotion styles in speech than that of MFCC.